Nanoparticle-Based Strain Gauges: Anisotropic Response Characteristics,Multidirectional Strain Sensing,and Novel Approaches to Healthcare Applications

Directional strain sensing is essential for advanced sensor applications in the field of human-machine interfaces and healthcare. Here, the angle dependent anisotropic strain sensitivity caused by charge carriers percolating through cross-linked gold nanoparticle (GNP) networks is studied and these versatile materials are used for the fabrication of wearable triaxial pulse and gesture sensors. More specifically, the anisotropic response of 1,9-nonanedithiol cross-linked GNP films is separated into geometric and piezoresistive contributions by fitting the measured data with an analytic model. Hereby, piezoresistive coefficients of g₁₁ ∼ 32 and g₁₂ ∼ 21 are extracted, indicating a slightly anisotropic response behavior of the GNP-based material. To use the material for healthcare applications, arrangements of three GNP transducers are patterned lithographically and fully embedded into a highly flexible silicone polymer (Dragon Skin 30). The new encapsulation method ensures good and robust electrical contacts and enables facile handling and protection from external influences. A facile read-out with wireless data transmission using off-the-shelf electrical components underlines the great potential of these devices for applications as skin-wearable healthcare sensors.     

Wear Behavior of Roller-Burnished High Interstitial Austenitic Stainless Steel Parts in Glass-Reinforced Plastic Melt

Herein this investigation, profiled high interstitial austenitic stainless steel parts are burnished on a profile-rolling machine, and afterward, the wear behavior is analyzed in a melt of glass-reinforced polypropylene. Wear and corrosion resistance are significant properties of steel parts in the plastics and food industries. The high work-hardening ability of high interstitial austenitic stainless steel enables burnishing parts with a significant local hardness to increase up to maximum values of ≈600 HV 1. Wear tests on a recently developed test stand reveal that the burnished austenitic stainless steel surface performs similarly to a nitrided surface of the standard nitriding steel 31CrMoV9 + QT with a hardness of ≈830 HV 0.5. Regarding the given advantage of corrosion resistance, it is concluded that roller burnishing supports the applicability of high interstitial austenitic stainless steel in plastics and food industries.     

Insights into Phase Evolution and Mechanical Behavior of the Eutectoid Ti–6.4(wt%)Ni Alloy Modified with Fe and Cr

Titanium alloys gain increasing importance in industry due to the expansion of advanced manufacturing technologies such as additive manufacturing. Conventional titanium alloys processed by such technologies suffer from formation of large primary grains and anisotropy of mechanical properties. Therefore, novel alloys are required. Herein, the effect of ternary alloying elements Fe and Cr on the Ti–6.4(wt%)Ni eutectoid system is investigated. Both elements act as eutectoid formers. Fe and Cr show sluggish transformation behavior, whereas Ni is an active eutectoid-forming element. Thereby, sluggish refers to slow and active to fast transformation kinetics. The focus of this work is on the combined addition of such elements studied under different heat-treatment conditions. It is shown in the results that largely varying microstructures can be generated resulting in hardness values ranging from 239 to 556 HV_0.1. Moreover, the formation of a substructure within the α phase of direct aged alloys is observed. The formation mechanism of this substructure is investigated in detail. The mechanical properties are discussed based on the microstructural characteristics. The presence of intermetallic Ti₂Ni phase increases the Young's modulus, whereas the presence of ω phase results in embrittlement. The results shed light upon the complex phase formation and decomposition behavior of titanium alloys based on Ti–6.4Ni.     

Workarounds as generative mechanisms for bottom-up process innovation—Insights from a multiple case study

Workarounds are goal-driven deviations from the standard operating procedures performed to overcome obstacles constraining day-to-day work. Despite starting as temporary fixes, they can become established across an organisation and trigger the innovation of processes and IT artefacts that can resolve misfits permanently. Although prior research has elicited antecedents and types of workarounds, it is not known how workarounds diffuse in an organisation and, thereby, innovating co-workers' activities, IT artefacts, and organisational structures. The results of our multiple two-year case study provide unique empirical insights into the diffusion of workarounds and how they can act as generative mechanisms for bottom-up process innovation.     

GrowliFlower: An image time-series dataset for GROWth analysis of cauLIFLOWER

In this paper, we present GrowliFlower, a georeferenced, image-based unmanned aerial vehicle time-series dataset of two monitored cauliflower fields (0.39 and 0.60 ha) acquired in 2 years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14,000 individual cauliflower plants. The coordinates enable the extraction of complete and incomplete time-series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre- and post-defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations. The proposed dataset was created to facilitate the development and evaluation of various machine-learning approaches. It focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to advance automation in agriculture. Two baseline results of instance segmentation tasks at the plant and leaf level based on labeled instance segmentation data are presented. The complete GrowliFlower dataset is publicly available ( http://rs.ipb.uni-bonn.de/data/growliflower/ ).